The value of your data depends on how well you organize and analyze it. As data gets more extensive and data sources more diverse, it becomes essential to review it for content and quality. However, only about 3% of data meets quality standards, which means companies with poorly managed data lose millions of dollars in wasted time, money, and untapped potential. 

That is where Data Profiling comes in — a powerful weapon to fight against bad data. It is the act of monitoring and cleansing data to improve data quality and gain a competitive advantage in the marketplace. In this article, we explore the process of data profiling, its definition, tools, and technologies, and look at ways how it can help businesses fix data problems.

What Is Data Profiling (DF)?

It is the process of examining source data and understanding structure, content, and interrelationships between data. The method uses a set of business rules and analytical algorithms to analyze data minutely for discrepancies. Data Analysts then use that information to interpret how those factors can align with business growth and objectives.  

Data profiling is increasingly vital for businesses as it helps determine data accuracy and validity, risks, and overall trends. It can eliminate costly errors that usually occur in customer databases, like missing values, redundant values, values that do not follow expected patterns, etc. Companies can use the valuable insight gained from data profiling to make critical business decisions.  

Most commonly, it  is used in combination with an ETL (Extract, Transform, and Load) process for data cleansing or data scrubbing and moving quality data from one system to another. An example can help you understand what is DF in ETL. Often ETL tools are used to move data to a data warehouse. Data profiling can come in handy to identify which data quality issues need to be fixed in the source and which issues can be fixed during the ETL process.   

Data analysts follow these steps:

  • Collection of descriptive statistics including min, max, count, sum
  • Collection of data types, length, and repeatedly occurring patterns
  • Tagging data with keywords, descriptions, types
  • Carrying out data quality assessment and risks of joining data
  • Discovering metadata and estimating accuracy
  • Identifying distributions, key candidates, functional and embedded-value dependencies, and performing inter table analysis
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Data Profiling Tools

Here's an in-depth look at each of the data profiling tools mentioned, including a brief overview, list of features, and pros:

1. Informatica Data Quality

Informatica Data Quality offers a comprehensive tool suite to ensure high-quality data across complex ecosystems. It focuses on delivering trustworthy, clean, and secure data to all stakeholders.

Features

  • Data quality management
  • Data profiling and cataloging
  • Data cleansing and standardization
  • Business rule management

Pros

  • Comprehensive data quality solutions
  • Advanced analytics for data insights
  • Scalable across various data volumes and types
  • Strong support for governance and compliance

2. Talend Open Studio

Talend Open Studio is an open-source data integration tool that also offers robust data profiling capabilities. It allows users to design and deploy data workflows quickly.

Features

  • Data integration and ETL capabilities
  • Data profiling and quality
  • Big data and cloud support
  • Extensive library of pre-built components

Pros

  • Free and open-source
  • User-friendly graphical interface
  • Supports a wide range of data sources and types
  • Community support and resources

3. IBM InfoSphere Information Analyzer

IBM InfoSphere Information Analyzer is a powerful tool for analyzing data quality, content, and structure. It is designed to provide detailed insights to improve data quality.

Features

  • Column analysis
  • Primary key and foreign-key analysis
  • Cross-domain analysis
  • Data quality assessments

Pros

  • Comprehensive and detailed data analysis
  • Supports a wide range of data sources
  • Integration with IBM's data management suite
  • Advanced reporting and visualization tools

4. SAP Business Objects Data Services (BODS)

SAP BODS combines data integration, quality, and profiling in one package. It enables users to transform, enrich, and manage data across enterprise landscapes.

Features

  • Data quality management
  • Data profiling and cleansing
  • Metadata management
  • ETL and real-time data processing

Pros

  • Integrated approach to data management
  • Powerful transformation and enrichment capabilities
  • Strong metadata management features
  • High scalability and performance

5. Informatica Data Explorer

Informatica Data Explorer is designed for deep data analysis, offering capabilities to discover anomalies and hidden relationships within data.

Features

  • Advanced data profiling
  • Anomaly detection
  • Relationship discovery
  • Pre-built rules for data analysis

Pros

  • Comprehensive data analysis tool
  • Supports structured and unstructured data
  • Powerful discovery capabilities
  • Integration with other Informatica products

6. Talend Open Studio for Data Quality

Similar to Talend Open Studio, this version focuses on data quality, allowing users to analyze and improve the integrity of their data without writing code.

Features

  • Data profiling and quality checks
  • Support for various data sources
  • Custom business rules
  • Data validation and cleansing

Pros

  • User-friendly and code-free
  • Versatile data support
  • Integration with Talend's broader data management suite
  • Free to download and use

7. Melissa Data Profiler

Melissa Data Profiler offers a suite of tools for ensuring high-quality data through profiling, enrichment, matching, and verification.

Features

  • Data profiling and analysis
  • Data enrichment and verification
  • Address and name validation
  • Data matching and deduplication

Pros

  • Comprehensive data quality solutions
  • Intuitive and easy to use
  • Supports a wide range of data types
  • Strong focus on data accuracy and consistency

8. Alteryx Designer

Alteryx Designer provides a drag-and-drop interface for data blending, preparation, and analysis to enhance data-driven decisions.

Features

  • Data blending and preparation
  • Advanced analytics and predictive modeling
  • Workflow automation
  • Integration with numerous data sources

Pros

  • User-friendly interface
  • Powerful analytics and modeling capabilities
  • Efficient workflow automation
  • Scalable for large datasets

9. SAP Information Steward

SAP Information Steward focuses on data governance and quality, providing tools for metadata management, data profiling, and quality monitoring.

Features

  • Data profiling and quality monitoring
  • Metadata management
  • Data governance and Stewardship
  • Integration with SAP environments

Pros

  • Strong data governance capabilities
  • Seamless integration with SAP solutions
  • Comprehensive data quality tools
  • Supports a collaborative approach to data management

10. Dataedo

Dataedo specializes in data documentation and metadata management, offering capabilities for data cataloging and business glossaries to improve data understanding.

Features

  • Data documentation and cataloging
  • Metadata management
  • Data profiling
  • Business Glossary

Pros

  • Enhances data understanding and visibility
  • Intuitive interface for non-technical users
  • Comprehensive documentation and reporting capabilities
  • Focus on collaboration and team-based documentation
  • Customizable to suit various data environments

Data Profiling Examples

Some DF examples in use today can be to troubleshoot problems within huge datasets by first examining metadata. For instance, you can use SAS metadata and data profile tools with Hadoop to identify and resolve issues within the data to find those data types that can best contribute to innovative business ideas. 

SAS data loader for Hadoop enables business users to profile Hadoop data sets using a visual interface and store the results. The profiling results in data quality metrics, graphic procedures, metadata measures, and other charts that facilitate the assessment of data and enhance data quality.    

DF tools can have real-world effects. For instance, the Texas Parks and Wildlife Department used the DF features of SAS data management to improve customer experience. They used DF tools to identify spelling errors, address standardization and geocoding attributes of data. The information thus collected helped to enhance the quality of customer data, offering a better opportunity to Texans to use the vast acres of parks and waterways available to them. 

Data Profiling Best Practices

There are three distinct components:

  • Structure Discovery – it helps to determine if data is consistent and has been formatted correctly. It uses basic statistics for information about data validity. 
  • Content Discovery – data is formatted, standardized, and correctly integrated with existing data efficiently and on time. For example, if the street address is wrongly formatted, there’s the risk of delivery getting misplaced or difficulty reaching customers. 
  • Relationship Discovery – identifies relations between various data sets

 Basic DF Practices Include:

Distinct count and percent – this technique identifies natural keys and unique values in each column that can help in case of inserts and updates. It is appropriate for tables without headers.

Percent of zero or blank or null values – users can use this practice to identify missing or unknown data. ETL architects set up default values using this approach. 

Maximum, Minimum, average string length – used to select suitable data types and sizes in the target database. Column widths can be set just wide enough to hold data to boost performance.

Advanced DF Practices Include:

1. Key Integrity – makes sure data always contains keys, using zero/blank/null analysis. It helps classify orphan keys, which can cause a problem for ETL and future analysis.

2. Cardinality – used to check relationships between related data sets such as one-to-one, one-to-many and many-to-many. This enables BI tools to perform inner or outer data joins appropriately. 

3. Pattern and Frequency distribution – this practice enables checking if data fields are correctly formatted. This is very important for data fields used for outbound communications like emails, phone numbers, and addresses. 

DF in Data Warehousing 

In today’s cloud-based data pipeline architecture, there’s an even higher prevalence of unstructured data. Automated data warehouses are used to tackle DF and preparation on their own. Instead of using a DF tool to analyze and for data quality management, analysts feed the data into an automated data warehouse where the data automatically gets cleaned, optimized, and prepared for analysis. 

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Conclusion

Data profiling is an essential process in the ETL (Extract, Transform, Load) pipeline, enabling organizations to analyze the quality and structure of their data before it's integrated into data warehouses or analytics platforms. By identifying inconsistencies, redundancies, and anomalies, data profiling helps ensure that data is accurate, reliable, and useful for decision-making. With the advent of big data and the increasing reliance on data-driven insights, the role of data profiling has become more critical than ever.

For professionals looking to delve deeper into the world of data analysis and ETL processes, enrolling in a comprehensive course like the Data Analyst Certification offered by Simplilearn is an excellent step forward. This course equips learners with the necessary skills and knowledge to navigate the complexities of data analysis, from data profiling to advanced analytics, making them invaluable assets to their organizations in today's data-driven world.

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